Electric Braking Research Articles

Estimating the effectiveness of electric vehicles braking when determining the circumstances of a traffic accident

In the vast majority of cases, the braking process is used to prevent traffic accidents. The effectiveness of this process depends on the design and functionality of vehicle braking systems (presence of anti-lock braking system, emergency braking system, preventive safety systems, etc.) and is limited by the amount of frictional forces in contact of tires with the road. The improvement of methodical approaches to evaluating the effectiveness of braking of cars contributes to increasing the accuracy and objectivity of establishing the circumstances of the occurrence of emergency situations. The paper analyses existing methods of evaluating the braking parameters of vehicles (including those with an electric drive) and modern methods of evaluating electric vehicle braking parameters and conducting auto-technical investigations of traffic accidents, which relate to using different methodological approaches and digital technologies at all stages of expert research. In contrast to existing models, the proposed mathematical model for estimating the trajectory of two-axle cars during braking allows for considering various types of input parameter uncertainty, reducing the range of possible modeling errors by 39%. Comparing simulation results and experimental data showed that the average relative error is 4.58%, and the maximum error did not exceed 7.82%. The performed study of the stability of the electric vehicles' movement during emergency braking with the help of developed mathematical models in the Mathcad software environment reveals the content of the algorithm of a similar calculation in specialized computer programs of auto technical examination. Conducting such calculations is relevant in the analysis of real accident situations, where specific circumstances and features that cannot be considered during modeling in specialized software must be taken into account. Simultaneously, the probability of type I errors is reduced by 2–19%, and type II errors are reduced by 43–68%.

Regenerative braking control strategy for pure electric vehicles based on fuzzy neural network

This study investigates the efficiency and safety of regenerative brake energy recuperation systems for electric vehicles. A three-input single-output fuzzy controller is developed to allocate hydraulic and electric braking forces, considering brake intensity, vehicle speed, and battery SOC's impact on regenerative braking performance. Fuzzy neural networks are utilized due to their effectiveness in solving complex, nonlinear, and fuzzy systems, along with their robustness to parameter changes and external disturbances. The fuzzy process of the controller is optimized using a self-tuning algorithm for designing membership function parameters, resulting in a fuzzy neural network controller. Moreover, electric and hydraulic braking forces are redistributed. Simulation using AVL Cruise software is conducted under NEDC and FTP-75 working conditions. The suggested brake energy recovery control approach using fuzzy neural networks successfully recovers braking energy, achieving energy recovery efficiencies of 14.52% and 39.61% under NEDC and FTP-75 conditions, respectively.

Study on multi-closed loop control of electro-mechanical braking for electric vehicles based on clamping force

In order to improve the clamping force control accuracy of electro-mechanical braking system of electric vehicles, a multi-closed loop control strategy of electro-mechanical braking based on clamping force is proposed. A detailed EMB mathematical model is established. The sliding mode speed controller and improved fuzzy PID clamping force controller are designed, and the joint simulation model of the speed the clamping force controller is established, and simulation experiments are used to verify the effectiveness of the control strategy. Comparative analysis of three simulation conditions, the maximum adjustment time of the proposed control strategy is 0.254 s and the maximum overshoot is 0.45%. The results of research show that the control strategy designed in this paper can quickly and stably reach the target value of clamping force, has a strong anti-interference capability, has some reference value in the electric vehicle braking control.

Integrated optimization of driving strategy and energy management for hybrid diesel multiple units

Hybrid diesel multiple units (HDMU) are considered an effective way to reduce fuel consumption and pollution emissions in regional non-electrified railways. To minimize the fuel consumption of HDMU, this paper formulates an integrated optimization of driving strategy and energy management. By introducing convex relaxation and linearization, we propose a second-order cone programming method to solve this integrated optimization. Case studies of multiple scenarios validate the effectiveness and robustness of the proposed method. In addition, the simulation results show that compared with the sequential optimization with the step-by-step improvement of driving strategy and energy management, the integrated optimization considers the battery characteristics when optimizing the driving strategy and reduces the electric braking power of the train, thus allowing the battery to absorb more braking energy and reduce fuel consumption.

Research Effectiveness of Regeneral Brake Energy on Toyota Prius Vehicles by Matlab/Simulink

Regenerative brake control creates an optimal synergy between mechanical and electrical braking. Based on the study of vehicle dynamics under braking conditions propose a new control mode that ensures the best braking performance and maximum braking energy recovery. The implementation of the above control mode requires a combination of the traction control model and the brake control system. The HEV power distribution model is built using Matlab/ Simulink and the simulation results have shown a significant improvement in fuel consumption when using the regenerative braking system.

Моделирование режима электрического торможения тепловоза с последовательным возбуждением коллекторных тяговых двигателей при использовании силового полупроводникового преобразователя

Purpose: To consider the structure, advantages and operating principle of a new transistor converter based on isolated gate bipolar transistors (IGBT) for diesel locomotives with commutator traction motors in the electric braking mode, as well as the implementation features of this mode on diesel locomotives. To show the functional structure of the converter automatic control system for the electric braking mode. To plot a potential braking characteristic of a diesel locomotive with a new converter. Methods: Simulation modeling of the electric braking mode using a computational mathematical model of a traction drive in the Matlab Simulink environment. Analysis of the simulation results of the traction drive operation in stationary and dynamic conditions. Comparison of the obtained braking characteristic with the existing one for 2TE25KM diesel locomotive. Results: It is established that the use of the proposed transistor traction converter in the power electrical circuit of a diesel locomotive provides axis-by-axis control of a commutator traction electric drive in the electric braking mode with series excitation of traction motors and allows to significantly increase the electric brake efficiency at low speeds. The proposed solution implements the target braking force of the locomotive in a wide range of speeds, as well as its limitations due to the electromechanical parameters of the diesel locomotive assemblies. Practical significance: It is shown that the proposed solution provides the increase of the electric brake efficiency, life extension of brake shoes and wheels of the rolling stock.

Practical hybrid model predictive control for electric pneumatic braking system with on-off solenoid valves

Accurate and rapid braking pressure regulation in electric pneumatic braking systems (EPBS) is vital to vehicle safety. Due to the switching behaviors of the on-off solenoid valves, the operation of the EPBS shows a hybrid nature with both continuous variables and discrete events, which raises the hybrid control problem. One of the possible solutions is to employ the hybrid model predictive controller with the mixed logical dynamical (MLD) model based on the linear approximation of the system dynamics. However, the nonlinearity and complexity of the EPBS make the MLD model obtained by linearizing the system equations directly require high storage and computing capacity. To address these issues, this article presents a practical hybrid model predictive controller based on the system dynamics simplified expressions considering the EPBS pressure variations caused by on-off solenoid valve states at the current sampling time and the last sampling time. The relationship between the pressure variations and the on-off solenoid valve states is first studied by the system mathematical model, followed by applying the mixed logic dynamical modeling approach to establish the hybrid model of the pressure continuous dynamics with discrete features of on-off solenoid valves. Based on these, a hybrid model predictive controller is formulated to solve the EPBS pressure control problem. The simulations and bench experiments are carried out to verify the controller. Besides, an existing model predictive control (MPC) controller is compared with the proposed controller. All the results demonstrate the effectiveness of the hybrid model predictive controller.

Comparison of the Flywheel Energy Storage Performance in the Tram Overhead Contact System in the Cold and Warm Transport Operation Seasons

The article discusses the results of statistical and field studies of the energy consumption modes by the traction and non-traction loads in the overhead contact system (OCS) of the on-ground urban electric transport vehicles in the warm and hot seasons and the associated changes in the operation modes of an NKE-3GT stationary buffer energy storage. It is shown that the reduction of non-traction load, which takes place mainly in the warm season, entails a redistribution in the amounts of surplus and useful energy of electric rolling stock regenerative braking toward surplus energy and, hence, a growth in the surplus regeneration energy flows through the buffer energy storage.

Unsupervised anomaly analysis-based manufacturing quality test and grading method for combine harvesters

Manufacturing quality tests mainly depend on manual methods, and most quality evaluations rely on the quality evaluation intervals, which lacks quantitative means for grading. As a consequence, it is challenging for the test process to find hidden quality problems early. In this paper, we conduct research on the test and grading method for combine harvester manufacturing quality and propose a hybrid method of “end-of-line test & secondary grading”, including a LabVIEW®-based test system and LOF (Local Outlier Factor)-based secondary grading method. The developed test system collects data from multiple working systems in a combine harvester and constructs the dataset. We further investigate eight unsupervised anomaly detection methods for grading harvester quality. Taking the evaluation indices of intracluster compactness (CP), intercluster separation (SP), and anomaly hit amount, we comprehensively assess these methods for different system performances in the harvester. The assessment results validate the LOF as being better among the eight anomaly detection methods, which obtains smaller CP integral values of 241.49, 433.51, and 309.93 and larger SP integral values of 383.82, 565.07, and 543.80 for the electrical system, hydraulic system, and braking performance datasets, respectively. Meanwhile, the LOF also obtains a good anomaly hit amount. Finally, the end-of-line test system integrates the advanced LOF-based grading method to quantitatively assess the harvester performance and identify potential quality-violated parts over time. The developed system proposes a solution for the automation and optimization demand in agricultural machinery manufacturing evaluation and provides technical guidance for product operation and maintenance management.

Improved Electrical Vehicle Regenerative Braking with a Bidirectional Converter

Improved Electrical Vehicle Regenerative Braking with a Bidirectional Converter

AMB-Supported Flywheel Battery for Electric Tractors: Structure Design and Driving Method

Active magnetic bearing (AMB) supported flywheel energy system, also called flywheel battery, is a mechanical battery storing kinetic energy in a rotating flywheel disk and has appealing features such as high specific power and energy, short time of charging/discharging, no pollution and long life span. In this paper, we apply AMB-supported flywheel battery as a new type of power battery to electric tractors, which absorbs wasted energy when the electric tractor brakes frequently and provide additional driving energy supplement when the electric tractor face an overload start, a sudden load mutations or field work resistance mutations. The structure design of the AMB-supported flywheel battery is introduced, including the axial/radial magnetic bearing, flywheel disk, the rotor and the motor. The driving method of the whole system is also analyzed. This design provides the reliability guarantee for the innovative application of the AMB-supported energy storage flywheel battery in electric tractors.

Analysis of Regenerative Braking Strategy for Electric Vehicles Based on Braking Intention

Improved energy recovery during braking is the goal of the distributed drive electric car regenerative braking system. A regenerative braking control strategy for electric vehicles based on braking intention is suggested in order to address various drivers’ braking intents and road adhesion circumstances. An electric vehicle regenerative braking model for distributed drives is created using the MATLAB/Simulink tools. On the basis of FTP-75 operating conditions and open road operating conditions, the efficacy and economy of the control strategy in this study are verified. According to the various road adhesion coefficients, the simulation results demonstrate that the control method in this work can appropriately divide the braking torque of the wheels on both sides of the coaxial wheel. The technique can successfully enhance the regenerative braking recovery of distributed drive electric vehicles as compared to the often employed regenerative braking control method.

System-Level Fault Tolerance Design Process of Dual-Winding Motors for Overheat in Integrated Electric Braking System of Autonomous Vehicle

System-Level Fault Tolerance Design Process of Dual-Winding Motors for Overheat in Integrated Electric Braking System of Autonomous Vehicle

Single Winding Type Determination of Dual Winding Three-Phase Motor Considering Overheat Problem in Integrated Electric Braking System of Autonomous Vehicles

This article introduces the single winding type selection process for a dual winding (DW) three-phase motor. Unlike the conventional DW three-phase motor design, the proposed single winding type selection process simultaneously considers the mechanical length determined by the single winding type and the motor overheating problem caused by faulty operation for each single winding type. The proposed single winding type selection process consists of four steps. First, all single-winding combinations, the point symmetry dual winding (PSDW), line symmetry dual winding (LSDW), and cross symmetry dual winding (CSDW) for DW three-phase motor are investigated. Second, according to the selected single winding types, the busbar height (BH) required for dual division is designed and compared. Third, the stack length (SL) of motor in the PSDW, the LSDW, and the CSDW obtained through overheat compensation design to avoid the overheating problem in the faulty mode are investigated and compared. In the overheat compensation design, the thermal map utilizing measurement data from the previously developed motor for an integrated electric brake (IEB) system, a radial basis function, an overheat compensation coefficient, and the IEB brake load condition are utilized. Finally, the single winding type of DW three-phase motor is selected as the CSDW by comparing the total motor size. As a result of the DW three-phase motor design, the proposed CSDW can reduce the motor size by 12.0% and 4.2%, respectively, compared with the PSDW and LSDW.

UKF-Based Observer Design for the Electric Brake Booster in Situations of Disturbance

The motor-driven electric brake booster (E-Booster) can replace the traditional vacuum booster to realize the braking power assistance and active braking. Independent of extra sensors, this paper proposes a full-state observer for E-Booster based on Unscented Kalman Filter (UKF) in the presence of a driver’s input force disturbance. The electro-hydraulic system is first modeled, which includes a nonlinear hydraulic model and the reaction disk’s rubber model. The pre-compression is designed to produce linear power assistance based on the properties of rubber material. With the existence of the disturbance, the linear quadratic regulator (LQR) algorithm is used to track the pre-compression of the reaction disk so that E-Booster is developed into a closed-loop system to achieve power assistance. The proposed UKF observer can online estimate the states considering the controller input and disturbance input. To reduce the process error, the hydraulic p-V characteristic is fitted using the recursive least squares (RLS) method before observation. Furthermore, the simulation test and vehicle tests are performed to validate the observation effect. In the closed-loop test, UKF decreases residual error by 16% when compared to the typical Extended Kalman Filter (EKF). The simulation results remain consistent with the experimental results, demonstrating the effectiveness of the proposed method.

Design of LQR-based FLC for the Optimal Regenerative Braking Controlling of Solar PV-based Electric Vehicle System

The article presents an analysis of the optimal control of speed and regenerative braking in an electric vehicle (EV). The presence of the continuous generating power is one of the major challenges for battery charging of electric vehicle. In order to meet the demand of continuous power, electric vehicles are driven by solar PV arrays. Initially, maximum power extraction (MPPT) is assessed from a solar photovoltaic (PV) array using the P&O method. The outputs of the solar PV array are integrated with electric vehicles. Such integration creates the problem of maintaining the power quality. The existing methods also address the issue of optimal speed and torque control with high transients and more settling time. In this article, an electric vehicle is modeled first and then operated in regenerative braking mode. To address the shortcomings of existing methods, the linear quadratic regulator (LQR) method is used; then, fuzzy logic controller (FLC)-based LQR is used for optimal speed and torque control during electric vehicle regenerative braking. In terms of reduced settling time and improved peak overshoot, optimal speed and regenerative torque of electric vehicles are better realized with LQR-based FLCs than with LQR controllers. In addition to this, stopping speed characteristics with various electrical parameters (voltage, current, and flux) have been analyzed with different methods in which LQR-based FLC shows its dominance to attain zero speed in regenerative mode with more sharp characteristics.

Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles.

Recent investigations of the electric braking booster (E-Booster) focus on its potential to enhance brake energy regeneration. A vehicle's hydraulic system is composed of the E-Booster and electric stability control to control the master cylinder and wheel cylinders. This paper aims to address the independent closed-loop control of the position and pressure as well as the maintenance of the pedal feel. To track both the reference signals related to piston displacement and the wheel cylinder pressure, an explicit model predictive control (MPC) is developed. First, the new flow model is introduced as the foundation for controller design and simulation. Next, in accordance with the operational conditions, the entire system is divided into three switchable subsystems. The three distributed MPCs are constructed based on the linearized subsystems, and a state machine is used to perform the state jump across the controllers. A linear piecewise affine control law can then be obtained by solving the quadratic program (QP) of explicit MPC. Afterwards, the non-linear extended Kalman filter including the recorded time-variant process noise is used to estimate all the state variables. The effectiveness of the explicit MPC is evidenced by the simulations compared with a single MPC in regenerative and dead-zone conditions. The proposed controller decreases the latency significantly by 85 milliseconds, which also helps to improve accuracy by 22.6%. Furthermore, the pedal feel remains consistent, even when factoring in the number of vibrations caused by the inherent hydraulic characteristic of pressure versus volume.

Cascaded fast terminal sliding mode control for UAV electric braking system

The anti-skid braking system of unmanned aerial vehicle has the characteristics of high-order nonlinearity, strong coupling, and time-varying parameters, which will seriously affect the taxiing performance of the unmanned aerial vehicle. This paper proposed a cascaded fast terminal sliding mode control method for unmanned aerial vehicle electric braking system, which can accurately track the optimal slip rate and ensure the stability of the braking system. First, the electric braking system model of unmanned aerial vehicle is established, which gets rid of the reliance on the ground binding coefficient fitting formulas. Second, combined with the concept of the inversion control, the cascaded fast terminal sliding mode controller is designed to achieve the accurate tracking of the slip rate and the reference braking force. The stability of the controller is proved by the Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by simulation results.

The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode

Machine learning (ML)-based methods have attracted great attention in the multi-objective optimization problems, which is the key challenge in the energy management strategy (EMS) of the multi-power hybrid system. Our recently published research in this journal verified the effectiveness and feasibility of a Deep Deterministic Policy Gradient (DDPG)-based EMS in the charge-sustaining (CS) stage of a multi-mode plug-in hybrid vehicle (PHEV). However, the application of ML-based-EMS in the charge-depletion (CD) stage and the regenerative braking mode of PHEV are still missing. This study proposes a discrete-continuous hybrid actions-based hierarchical EMS to optimally distribute the dual-motor driving force in battery electric driving and regenerative braking. In the upper layer of EMS, DDPG is trained to learn the torque distribution principles of dual-motor operation to achieve better energy efficiency without losing dynamic performance. Meanwhile, the total recoverable braking torque is also determined by the upper layer EMS considering the braking demand, mechanical and electrical braking system conditions, vehicle safety, and the provisions of law. In the lower level of EMS, the driving mode is determined under the guidance of energy consumption optimization. The verified results show that the proposed EMS outperforms other deep reinforcement learning (DRL)-based hierarchical and non-hierarchical EMSs.

Axial Unipolar Eddy Current Brake Performance Characteristics Against Heat Increase in Rotor

The development of transportation technology in the automotive sector such as electric vehicles is increasingly advanced. One technology that is needed quite a lot is the development of supporting technology for electric vehicle braking. The use of regenerative braking on light electric vehicles such as 2-wheeled vehicles is not efficient because of its low weight. The use of Eddy Current Brake (ECB) can be a solution for braking support needs. This is because the ECB is a braking system that has the advantage of a lightweight design but still relies on the frictionless principle. However, in addition to its advantages, the eddy current brake is still in the early stages of its research with efficiency that still needs to be developed. In the discussion of the ECB, heat generation is one of the interesting topics to be discussed. Specifically, the study of the characteristics of the unipolar ECB axial performance on heat generation events has not yet been discussed. So this article aims to discuss these events with a simulation process and simple mathematical calculations. Design optimization is done to get the best value. As a result, the use of eddy current brakes with conductor disks using slots, can improve the performance of the ECB on the torque side and cooling side. Thus, this article is a good contribution to the sustainability of ECB research in both the general and automotive fields.